research-article

Sierra: a SIMD extension for C++

Authors:

Immanuel Haffner,

Sebastian HackAuthors Info & Claims

WPMVP '14: Proceedings of the 2014 Workshop on Programming models for SIMD/Vector processing

Pages 17 - 24

https://doi.org/10.1145/2568058.2568062

Published: 16 February 2014 Publication History

Abstract

Nowadays, SIMD hardware is omnipresent in computers. Nonetheless, many software projects make hardly use of SIMD instructions: Applications are usually written in general-purpose languages like C++. However, general-purpose languages only provide poor abstractions for SIMD programming enforcing an error-prone, assembly-like programming style. An alternative are data-parallel languages. They indeed offer more convenience to target SIMD architectures but introduce their own set of problems. In particular, programmers are often unwilling to port their working C++ code to a new programming language.

In this paper we present Sierra: a SIMD extension for C++. It combines the full power of C++ with an intuitive and effective way to address SIMD hardware. With Sierra, the programmer can write efficient, portable and maintainable code. It is particularly easy to enhance existing code to run efficiently on SIMD machines.

In contrast to prior approaches, the programmer has explicit control over the involved vector lengths.

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Cited By

Tayeb HPaillat LBramas B(2023)Autovesk: Automatic Vectorized Code Generation from Unstructured Static Kernels Using Graph TransformationsACM Transactions on Architecture and Code Optimization10.1145/363170921:1(1-25)Online publication date: 9-Nov-2023
https://dl.acm.org/doi/10.1145/3631709
Rosemann JMoll SHack S(2021)An abstract interpretation for SPMD divergence on reducible control flow graphsProceedings of the ACM on Programming Languages10.1145/34343125:POPL(1-31)Online publication date: 4-Jan-2021
https://dl.acm.org/doi/10.1145/3434312
Yadav SGupta NReverdell AKaiser H(2021)Parallel SIMD - A Policy Based Solution for Free Speed-Up using C++ Data-Parallel Types2021 IEEE/ACM 6th International Workshop on Extreme Scale Programming Models and Middleware (ESPM2)10.1109/ESPM254806.2021.00008(20-29)Online publication date: Nov-2021
https://doi.org/10.1109/ESPM254806.2021.00008
Show More Cited By

Index Terms

Sierra: a SIMD extension for C++
1. Computing methodologies
  1. Parallel computing methodologies
    1. Parallel programming languages
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language types
        Parallel programming languages

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Published In

cover image ACM Conferences

WPMVP '14: Proceedings of the 2014 Workshop on Programming models for SIMD/Vector processing

February 2014

94 pages

ISBN:9781450326537

DOI:10.1145/2568058

General Chairs:
Gabriel Tanase
IBM Research
,
Peng Wu
IBM Research
,
Joel Falcou
MetaScale & Université Paris Sud
,
Program Chairs:
Gabriel Tanase
IBM Research
,
Peng Wu
IBM Research

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 16 February 2014

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PPoPP '14

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SIGPLAN

PPoPP '14: ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

February 16, 2014

Florida, Orlando, USA

Acceptance Rates

WPMVP '14 Paper Acceptance Rate 12 of 18 submissions, 67%;

Overall Acceptance Rate 20 of 30 submissions, 67%

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Citations

Cited By

Tayeb HPaillat LBramas B(2023)Autovesk: Automatic Vectorized Code Generation from Unstructured Static Kernels Using Graph TransformationsACM Transactions on Architecture and Code Optimization10.1145/363170921:1(1-25)Online publication date: 9-Nov-2023
https://dl.acm.org/doi/10.1145/3631709
Rosemann JMoll SHack S(2021)An abstract interpretation for SPMD divergence on reducible control flow graphsProceedings of the ACM on Programming Languages10.1145/34343125:POPL(1-31)Online publication date: 4-Jan-2021
https://dl.acm.org/doi/10.1145/3434312
Yadav SGupta NReverdell AKaiser H(2021)Parallel SIMD - A Policy Based Solution for Free Speed-Up using C++ Data-Parallel Types2021 IEEE/ACM 6th International Workshop on Extreme Scale Programming Models and Middleware (ESPM2)10.1109/ESPM254806.2021.00008(20-29)Online publication date: Nov-2021
https://doi.org/10.1109/ESPM254806.2021.00008
Tasos AFranco JDrossopoulou SWrigstad TEisenbach S(2020)Reshape your layouts, not your programs: A safe language extension for better cache localityScience of Computer Programming10.1016/j.scico.2020.102481(102481)Online publication date: May-2020
https://doi.org/10.1016/j.scico.2020.102481
Amiri HShahbahrami A(2019)SIMD programming using intel vector extensionsJournal of Parallel and Distributed Computing10.1016/j.jpdc.2019.09.012Online publication date: Sep-2019
https://doi.org/10.1016/j.jpdc.2019.09.012
Springer MMasuhara H(2018)Ikra-CppProceedings of the 2018 4th Workshop on Programming Models for SIMD/Vector Processing10.1145/3178433.3178439(1-9)Online publication date: 24-Feb-2018
https://dl.acm.org/doi/10.1145/3178433.3178439
Mercadier DDagand PLacassagne LMuller G(2018)UsubaProceedings of the 2018 4th Workshop on Programming Models for SIMD/Vector Processing10.1145/3178433.3178437(1-8)Online publication date: 24-Feb-2018
https://dl.acm.org/doi/10.1145/3178433.3178437
Moss ASchluntz RBuhr P(2018)C: Adding modern programming language features to CSoftware: Practice and Experience10.1002/spe.262448:12(2111-2146)Online publication date: 20-Aug-2018
https://doi.org/10.1002/spe.2624
Bramas B(2017)InastempScientific Programming10.1155/2017/54824682017Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1155/2017/5482468
Reiche OKobylko CHannig FTeich J(2017)Auto-vectorization for image processing DSLsACM SIGPLAN Notices10.1145/3140582.308103952:5(21-30)Online publication date: 21-Jun-2017
https://dl.acm.org/doi/10.1145/3140582.3081039
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